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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik Jurnal Ilmu Komputer dan Informasi Jurnal Masyarakat Informatika Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik JUTI: Jurnal Ilmiah Teknologi Informasi Prosiding SNATIF Journal of ICT Research and Applications Teknika: Jurnal Sains dan Teknologi Jurnal Informatika dan Teknik Elektro Terapan Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika SISFOTENIKA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer English Language and Literature International Conference (ELLiC) Proceedings Infotekmesin Jurnal Mnemonic Abdimasku : Jurnal Pengabdian Masyarakat SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Journal of Soft Computing Exploration Advance Sustainable Science, Engineering and Technology (ASSET) Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Advance Sustainable Science, Engineering and Technology (ASSET) INOVTEK Polbeng - Seri Informatika Journal of Soft Computing Exploration
Christy Atika Sari
Teknik Informatika S1, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences Other

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A ROBUST AND IMPERCEPTIBLE FOR DIGITAL IMAGE ENCRYPTION USING CHACHA20 Nugroho, Widhi Bagus; Susanto, Ajib; Sari, Christy Atika; Rachmawanto, Eko Hari; Doheir, Mohamed
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1470

Abstract

In the current era, data security is mandatory because it protects our personal data from being used by irresponsible people. The objective of this research is to show the robustness of the method we propose to encrypt images using the chacha20 algorithm which is included in the symmetric encryption cryptography technique and uses one key for both encryption and decryption processes. we use the encryption method by reading the bits from a digital image which is processed using the chacha20 algorithm to get the results of the digital image encryption. The results of this study indicate that the Chacha20 algorithm is secure to use when encrypting and decrypting digital images. The average MSE value generated by the chacha20 algorithm is 0.1232. The average PSNR value is 57.4784. The average value of UACI is 49.99%. The average value of NPCR is 99.602%. The test values were acquired by executing encryption and decryption processes on 5 distinct colour digital images with different size. Additionally, this study displays histograms for the original digital image, as well as for the encrypted and decrypted digital images, illustrating the pixel distribution in each. The histogram also serves as material for analysis of the success of the encryption and decryption processes in digital images.
VGG-16 ARCHITECTURE ON CNN FOR AMERICAN SIGN LANGUAGE CLASSIFICATION Meitantya, Mutiara Dolla; Sari, Christy Atika; Rachmawanto, Eko Hari; Ali, Rabei Raad
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2160

Abstract

Every country has its sign language such as in Indonesia there are 2 types namely Indonesian Sign Language System called SIBI and BISINDO (Indonesian Sign Language). American Sign Language (ASL) is a sign language that is widely used in the world. In this research, the classification of American Sign Language (ASL) using the Convolutional Neural Network (CNN) method using VGG-16 architecture with Adam optimizer. The data used is 14000 ASL image data with 28 classes consisting of letters A to Z plus space and nothing with a division of 90% training data and 10% validation data. From this research, the overall accuracy is obtained with a value of 98% and the accuracy value of validation data evaluation is 89.07%.
CLASSIFICATION OF ORGANIC AND NON-ORGANIC WASTE WITH CNN-MOBILENET-V2 Oktayaessofa, Eqania; Sari, Christy Atika; Rachmawanto, Eko Hari; Yaacob, Noorayisahbe Mohd
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2165

Abstract

Data from the Ministry of Environment and Forestry shows that the amount of organic and non-organic waste in 2023 has started to decline compared to the previous year. However, waste management in the central landfill is still not optimal. This is a problem for the community and the environment because it can cause pollution and disrupt public health around the disposal site. The reason for the difficulty of waste management at the landfill is that people still dispose of waste without separating it first. In addition, it is also due to a lack of public awareness and knowledge. One of the things that can be done to help overcome the problem of waste and its management is to develop an application that can help people understand the importance of waste selection and facilitate socialization in the community. For that, a model is needed that can classify waste based on its type with accurate accuracy. In this study, we propose a deep learning model, CNN with mobilenetV2 architecture, to classify organic and non-organic waste. This model uses a dataset consisting of 4380 images of organic and non-organic waste. Then 3 preprocessing stages were carried out, namely resize, normalization, and augmentation. From this process, data training was carried out and researchers obtained model evaluation results with 98.47% accuracy, 97% precision, 97% recall, and 97% F1 Score evaluation results. These results show that the proposed model is categorized as excellent.
Enhancing MPEG-1 Video Quality Using Discrete Wavelet Transform (DWT) with Coefficient Factor and Gamma Adjustment Krismawan, Andi Danang; Susanto, Ajib; Rachmawanto, Eko Hari; Muslih, Muslih; Sari, Christy Atika; Ali, Rabei Raad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4422

Abstract

Low-quality video caused by compression artifacts, noise, and loss of detail remains a significant challenge in video processing, affecting applications in streaming, surveillance, and medical imaging. Existing enhancement techniques often struggle with excessive noise amplification or high computational complexity, making them inefficient for real-time applications. This study proposes an improved video enhancement method using Discrete Wavelet Transform (DWT) with optimized coefficient factor and gamma adjustment. DWT is a mathematical approach that decomposes video frames into frequency subbands, enabling selective enhancement of important details. To analyze the impact of different wavelets, this study evaluates Coif5, db1, sym4, and sym8 wavelets. The sym8 wavelet, known for its high symmetry and ability to minimize artifacts, achieves the best results in preserving fine details and structural integrity. The coefficient factor is dynamically adjusted to sharpen details while preventing noise amplification, and gamma adjustment is applied to optimize brightness and contrast. The proposed method was evaluated using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Experimental results show that sym8 wavelet with gamma 0.7 and coefficient factor 0.3 provides the best balance, achieving an MSE of 0.062, a PSNR of 12.050 dB, and an SSIM of 0.674, outperforming Coif5, db1, and sym4 wavelets. The results indicate that wavelet selection significantly impacts video enhancement performance, with sym8 providing superior contrast enhancement and noise suppression. This study contributes to real-time video processing and AI-based applications, ensuring enhanced visual quality with minimal computational overhead.
Securing Medical Images Using Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) for Image Steganography Pramudya, Elkaf Rahmawan; Handoko, L. Budi; Harjo, Budi; Sani, Ramadhan Rakhmat; Sari, Christy Atika; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Sarker, Md. Kamruzzaman
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4426

Abstract

Steganography is a technique for embedding secret information into digital media, such as medical images, without significantly affecting their visual quality. The primary challenge in medical image steganography is preserving the quality of the cover image while ensuring robustness against distortions such as compression or data manipulation attacks, which may impact diagnostic accuracy. This study proposes an enhanced steganographic method based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) to improve the security and robustness of medical image embedding. DWT decomposes the medical image into four frequency sub-bands (LL, LH, HL, HH), while SVD is applied to embed the secret image while maintaining essential medical features. Experimental results show that the proposed method achieves a PSNR value of up to 78 dB and an SSIM value approaching 1, indicating that the stego image quality is nearly identical to the original cover image. Compared to previous DCT-SVD and IWT-SVD-based approaches, the DWT-SVD method offers superior robustness and imperceptibility, particularly in preserving image quality in complex-textured medical images. This method contributes to enhancing data security in telemedicine and AI-based medical imaging applications by ensuring that sensitive medical data remains protected while preserving image integrity for diagnostic use.
Hiragana Character Classification Using Convolutional Neural Networks Methods based on Adam, SGD, and RMSProps Optimizer Mulyono, Ibnu Utomo Wahyu; Kusumawati, Yupie; Susanto, Ajib; Sari, Christy Atika; Islam, Hussain Md Mehedul; Doheir, Mohamed
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.2313

Abstract

Purpose: Hiragana image classification poses a significant challenge within the realms of image processing and machine learning. Despite advances, achieving high accuracy in Hiragana character recognition remains elusive. In response, this research attempts to enhance recognition precision through the utilization of a Convolutional Neural Network (CNN). Specifically, the study explores the efficacy of three distinct optimizers like Adam, Stochastic Gradient Descent with Momentum (SGDM), and RMSProp in improving Hiragana character recognition accuracy. Methods: This research adopts a systematic approach to evaluate the performance of a Convolutional Neural Network (CNN) in the context of Hiragana character recognition. A meticulously prepared dataset is utilized for in-depth testing, ensuring robustness and reliability in the analysis. The study focuses on assessing the effectiveness of three prominent optimization methods: Stochastic Gradient Descent (SGD), RMSProp, and Adam. Result: The results of the model performance evaluation show that the highest accuracy was obtained from the RMSP optimizer with an F1-Score reaching 99.70%, while the highest overall accuracy was 99.87% with the Adam optimizer. The analysis is carried out by considering important metrics such as precision, recall, and F1-Score for each optimizer. Novelty: The performance results of the developed model are compared with previous studies, confirming the effectiveness of the proposed approach. Overall, this research makes an important contribution to Hiragana character recognition, by emphasizing the importance of choosing the right optimizer in improving the performance of image classification models.
Hybrid Quantum Representation and Hilbert Scrambling for Robust Image Watermarking Sari, Christy Atika; Abdussalam, Abdussalam; Rachmawanto, Eko Hari; Islam, Hussain Md Mehedul
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.10140

Abstract

Purpose: This work aims to apply Quantum Hilbert Scrambling to enhance the security and integrity of image watermarking without affecting visual quality degradation. Further conception of the surveyed methods could result in a very good solution to conventional methods of watermarking in solving some problems of digital image security and integrity with new concepts of quantum computing. Methods: The paper reviews Quantum Hilbert Scrambling, whose computational complexity is . The process involves encoding the image into a quantum state, permuting qubits by the Hilbert curve, and embedding a watermark using quantum gates. Result: The quantitative performance evaluation metrics, like Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM), have shown high Peak Signal to Noise Ratio (PSNR) values from 56.13 dB to 57.87 dB and Structural Similarity Index (SSIM) from 0.9985 to 0.9990, correspondingly. This justifies the fact that the quality degradation is very slight and the fine details of the structure are well maintained. Novelty: The proposed method uniquely integrates quantum computing with traditional watermarking steps for a secure and effective approach in digital watermarking. Further development should focus on improving the quantum circuit regarding computation efficiency, extending the applicability of the method to a wide range of images, and various situations in watermarking, and finding hybrid approaches by combining quantum and classical approaches towards better performance and scalability.
A Comparative Study of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19 Susanto, Ajib; Sari, Christy Atika; Rachmawanto, Eko Hari; Mulyono, Ibnu Utomo Wahyu; Mohd Yaacob, Noorayisahbe
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.47305

Abstract

Purpose: Javanese script is a legacy of heritage or heritage in Indonesia originating from the island of Java needs to be preserved. Therefore, in this study, the classification and identification process of Javanese script letters will be carried out using the CNN method. The purpose of this research is to be able to build a model which can properly classify Javanese script, it can help in the process of recognizing letters in Javanese script easily.Methods: In this study, the Javanese script classification process has been used the transfer learning process of Convolutional Neural Network, namely GoogleNet, DenseNet, ResNet, VGG16 and VGG19. The purpose of using transfer learning is to improve the sequential CNN model, processing can be better and optimal because it utilizes a previously trained model.Result: The results obtained after testing in this study are using the transfer learning method, the GoogleNet model gets an accuracy of 88.75%, the DenseNet model gets an accuracy of 92%, the ResNet model gets an accuracy of 82.75%, the VGG16 model gets an accuracy of 99.25% and the VGG19 model gets an accuracy of 99.50%.Novelty: In previous studies, it is still very rare to discuss the Javanese script classification process using the CNN transfer learning method and which method is the most optimal for performing the Javanese script classification process. In this study, it had been resulted find an effective method to be able to carry out the Javanese script classification process properly and optimally.
Capital Optical Character Recognition Using Neural Network Based on Gaussian Filter Astuti, Erna Zuni; Sari, Christy Atika; Syabilla, Mutiara; Sutrisno, Hendra; Rachmawanto, Eko Hari; Doheir, Mohamed
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.43438

Abstract

Purpose: As digital technology advances, society needs to convert physical text into digital text. There are now many methods available for doing this. One of them is OCR (Optical Character Recognition), which can scan images [1]–[4] containing writing and turn them into digital text, making it easier to copy written text from an image. Text recognition in images is complex due to variations in text size, color, font, orientation, background, and lighting conditions.Methods: The technique of text recognition or optical character recognition (OCR) in images can be done using several methods, one of which is a neural network or artificial neural network. The artificial neural network method can help a computer make intelligent decisions with limited human assistance. Intelligent decisions can be made because the neural network can learn and model the relationship between nonlinear and complex input and output data. In this research, the scaled conjugated gradient is applied for optimization. SCG is very effective in finding the minimum value of a complex function, but it takes longer than some other optimization algorithms.Result/Findings: The dataset used is an image with a size of 28 x 28 which is changed in dimension to 784 x 1. This research uses 4000 epochs and obtained the best validation result at epoch 3506 with a value of 0.0087446. Results: From the statistical test results, the effect of perceived usefulness on ease of use has the highest level of influence, obtaining a test value of 3.6. Furthermore, the effect of the attitude towards using on the behavioral intention to use has the lowest level of influence, which obtained a test value of 1.2.Novelty:  In this article, Gaussian filter is used as feature extraction to improve yield. Character detection results using a Gaussian filter are known to be almost 10% higher than those using only a neural network. The result with the Neural Network alone is 82.2%, while the Neural Network-Gaussian Filter produces 92.1%.
Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS Astuti, Erna Zuni; Sari, Christy Atika; Rachmawanto, Eko Hari; Ali, Rabei Raad
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.47364

Abstract

Purpose: Fraud is rampant in the current era, especially in the era of technology where there is now easy access to a lot of information. Therefore, everyone needs to be able to sort out whether the information received is the right information or information that is fraudulent. In this research, the process of classifying messages including ham or spam has been carried out. The purpose of this research is to be able to build a model that can help classify messages. The purpose of this research is also to determine which machine learning method can accurately and efficiently perform the ham or spam classification process on messages.Methods: In this research, the ham or spam classification process has been using machine learning methods. The machine learning methods used are the classification process with Random Forest, Logistic Regression, Support Vector Classification, Gradient Boosting, and XGBoost Classifier algorithms. Results: The results obtained after testing in this study are the classification process using the Random Forest algorithm getting an accuracy of 97.28%, Logistic Regression getting an accuracy of 94.67%, with Support Vector Classification getting an accuracy of 97.93%, and using XGBoost Classifier getting an accuracy of 96.47%. The best precision value obtained in this study is 98% when using the random forest algorithm. The best recall value is 94% when using the SVC algorithm. While the best f1-score value is 95% when using the SVC algorithm.Novelty: This research has been compared with several algorithms. In previous research, it is still very rarely done using XGBoost to classify the ham or spam in messages. We focus on giving brief information based con comparison algorithm and show the best algorithm to classify classify the ham or spam in messages. And for the novelty that exists from this research, the machine learning model built gets better accuracy when compared to previous research.
Co-Authors AA Sudharmawan, AA Abdul Qohhar Abdul Syukur Abdussalam Abdussalam Abdussalam Abdussalam, Abdussalam Abiyyi, Ryandhika Bintang Ahmad Salafuddin Ajib Susanto Akbar, Fadhilah Aditya Akbar, Ilham Januar Alfany, Fauzan Maulana Ali, Rabei Raad Alifia Salwa Salsabila Alvian Ideastari, Nukat Alvin Faiz Kurniawan Anak Agung Gede Sugianthara Andi Danang Krismawan Anggraeny, Tiara Anidya Nur Latifa Annisa Sulistyaningsih Anny Yuniarti Antonius Erick Handoyo Arditya Prayogi Arfian, Aldi Azmi Ariq Arsalan Aris Marjuni Aristides Bima Wintaka Ariza, Said Fachri Aryanta, Muhammad Syifa Aryaputra, Firman Naufal Astuti, Yani Parti Auni, Amelia Gizzela Sheehan Bambang Sugiarto Briliantino Abhista Prabandanu Budi Harjo Cahaya Jatmoko Cahyo, Nur Ryan Dwi Candra Irawan Candra Irawan Castaka Agus Sugianto Chaerul Umam Chaerul Umam Cinantya Paramita D.R.I.M. Setiadi Danang Krismawan, Andi Danang Wahyu Utomo Danar Bayu Adi Saputra Danu Hartanto Daurat Sinaga Daurat Sinaga De Rosal Ignatius Moses Setiadi Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Didik Hermanto Doheir, Mohamed Doheir, Mohamed Doheir, Mohamed A S Dwi Puji Prabowo Edi Faisal Egia Rosi Subhiyakto Egia Rosi Subhiyakto Eko Hari Rachmanto Eko Hari Rachmawanto Eko Septyasari Elkaf Rahmawan Pramudya Ericsson Dhimas Niagara Erika Devi Udayanti Erlin Dolphina Erna Daniati Erna Zuni Astuti Erna Zuni Astuti Ery Mintorini Etika Kartikadarma Farrel Athaillah Putra Feri Agustina Fidela Azzahra Florentina Esti Nilawati Florentina Esti Nilawati Florentina Esti Nilawati Folasade Olubusola Isinkaye Folasade Olubusola Isinkaye Gede Pradistya Evan Aryaputra Giovani Ardiansyah Gumelar, Rizky Syah Guruh Fajar Shidik Gusta, Muhammad Bima Hadi, Heru Pramono Haqikal, Hafidz Haris Pujianto Hartono, Matthew Raymond Haryanto, Christanto Antonius Haryanto, Christanto Antonius Hasbi, Hanif Maulana Hayu Wikan Kinasih Heru Lestiawan Hidayah Rahmalan Hidayah Rahmalan Himawan, Reyshano Adhyarta Hussain Md Mehedul Islam Hyperastuty, Agoes Santika Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ifan Rizqa Ihya Ulumuddin, Dimas Irawan Ikhsanuddin, Rohmatulloh Muhamad Imam Prayogo Pujiono Inzaghi, Reza Bayu Ahmad Isinkaye, Folasade Olubusola Islam, Hussain Md Mehedul Istiqomah, Annisa Ayu Ivan Stepheng Kamila, Izza Putri Kas Raygaputra Ilaga Kholifatun, Isnaeni Krismawan, Andi Danang Kumala, Raffa Adhi Kurniawan, Nicholas Alfandhy Kusuma, Edi Jaya Kusuma, Mohammad Roni Kusumawati, Yupie L. Budi Handoko Laksana, Deddy Award Widya Lalang Erawan Liya Umaroh Liya Umaroh, Liya Lucky Arif Rahman Hakim Mabina, Ibnu Farid Maulana Malik Ibrahim Al-Ghiffary Maxentia Kathleen Md Kamruzzaman Sarker Md Kamruzzaman Sarker Md Kamruzzaman Sarker Megan Febriana Putri Johana Mehta Pradnyatama Meitantya, Mutiara Dolla Mohamed A. S. Doheir Mohamed Doheir Mohamed Doheir Mohammad Rizal, Mohammad Mohd Yaacob, Noorayisahbe Muchamad Akbar Nurul Adzan Muhammad Eswin Bakkar Muhammad Khanif Naufal Muhammad Rikzam Kamal Mulyono, Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Wahyu Munandar Rahmat Prayogi Munis Zulhusni Musab Iqtait Musab Iqtait Musfiqur Rahman Sazal Muslih Muslih Nabila, Qotrunnada Neni Kurniawati Ningrum, Amanda Prawita Nisa, Yuha Aulia Noor Ageng Setiyanto Noor Ageng Setiyanto, Noor Ageng Noorayisahbe Mohd Yaacob Noorayisahbe Mohd Yacoob Nova Rijati Nova Rijati Nugroho, Widhi Bagus Nur Ryan Dwi Cahyo Oktaridha, Harwinanda Oktayaessofa, Eqania Ozagastra Caluella Prambudi Ozagastra Caluella Prambudi parti astuti, yani Parti Astuti, Yani Parti Astuti, Yani Parti Astuti1, Yani Parti Astuti1, Yani Permana langgeng wicaksono ellwid putra Pradana, Luthfiyana Hamidah Sherly Pradana, Rizky Putra Praskatama, Vincentius Pratama, Zudha Pratiwi, Saniya Rahma Pulung Nurtantio Andono Purwanto Purwanto Purwanto Purwanto Puspa, Silfi Andriana Putri Mega Arum Wijayanti Raafiandy Wirawan Avicenna Rabei Raad Ali Rabei Raad Ali Rahmalan, Hidayah Raihan Ramadhan Hamzah Raisul Umah Nur Ramadhan Rakhmat Sani Ratih Ariska Rizka Dian Safitri Rizky Damara Ardy Robert Setyawan Sabilillah, Ferris Tita Saifullah, Zidan Salma Shafira Fatya Ardyani Sania, Wulida Rizki Santoso, Bagus Raffi Sari, Wellia Shinta Sari Shinta Sarker, Md Kamruzzaman Sarker, Md. Kamruzzaman Setiarso, Ichwan Setiawan, Fachruddin Ari Shelomita, Viki Ari Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sofyan, Ega Adiasa Solichul Huda, Solichul Sudibyo, Usman Sudibyo, Usman Sudibyo, Usman Sumarni Adi, Sumarni Suprayogi Suprayogi Suprayogi Suprayogi Sutrisno, Hendra Syabilla, Mutiara Tan Samuel Permana Tan Samuel Permana Tiara Anggraeny Titien Suhartini Sukamto Umah Nur, Raisul Umaroh, Liya Umaroh, Liya Utomo, Danang Wahyu Velarati, Khoirizqi Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Yaacob, Noorayisahbe Mohd Yani Parti Astuti Yupie Kusumawati Zaenal Arifin Zahra Ghina Syafira